How to Install Faiss for GPU on Windows?

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Faiss (Facebook AI Similarity Search) is a powerful library developed by Facebook AI Research that is widely used for efficient similarity search and clustering of dense vectors. If you’re working with large-scale vector data, Faiss can significantly speed up your nearest neighbor search tasks, especially when combined with GPU acceleration. While Faiss installation on Linux is relatively straightforward, installing Faiss for GPU on Windows can be more challenging due to lack of official pre-built binaries and CUDA-related dependencies. So, the question is: How to install Faiss for GPU on Windows?

In this comprehensive guide, we’ll walk you through the prerequisites, methods, and best practices to get Faiss running with GPU support on your Windows system. Whether you’re a data scientist, researcher, or ML engineer, this step-by-step tutorial will help you set up Faiss for GPU-based vector search on Windows.

Why Use Faiss with GPU on Windows?

Running Faiss on GPU offers massive performance gains, especially when dealing with millions or billions of high-dimensional vectors. If your application involves tasks like:

  • Semantic search
  • Embedding-based retrieval
  • Recommendation systems
  • Image or video similarity search

…then GPU acceleration is essential for real-time performance. While most documentation and community support is Linux-centric, many developers use Windows machines for development and testing. Hence, having Faiss with GPU support on Windows can streamline workflows without requiring a Linux virtual machine or dual-boot setup.

Prerequisites

Before installing Faiss with GPU support on Windows, make sure you have the following:

  • Operating System: Windows 10 or 11 (64-bit)
  • CUDA Toolkit: Version 10.2, 11.x, or later (installed and configured)
  • NVIDIA GPU: With compute capability 3.5 or higher (e.g., GTX 1080, RTX 2080, A100)
  • Python: Version 3.8 or newer
  • Anaconda/Miniconda: Strongly recommended for managing environments
  • Visual Studio: C++ Build Tools (recommended: Visual Studio 2019)
  • Git and CMake: For building Faiss from source

Option 1: Using Windows Subsystem for Linux (WSL2)

Pros:

  • Compatible with Linux-native Faiss build
  • Easier GPU support through WSL2 + CUDA drivers

Steps:

  1. Enable WSL and install Ubuntu
wsl --install
  1. Install CUDA Toolkit inside WSL Follow NVIDIA’s official WSL installation guide to enable GPU support.
  2. Install Faiss with GPU in WSL2
sudo apt update
sudo apt install build-essential cmake git libopenblas-dev libomp-dev
conda create -n faiss-gpu python=3.10
conda activate faiss-gpu
conda install -c pytorch faiss-gpu
  1. Test Installation
import faiss
faiss.StandardGpuResources()
print("Faiss GPU installed and available")

Note: This is the most reliable way to use Faiss with GPU on a Windows machine.

Option 2: Building Faiss for GPU Natively on Windows

Although challenging, it is possible to build Faiss for GPU natively on Windows using Visual Studio and CMake.

Step-by-Step Guide:

1. Clone Faiss Source

git clone https://github.com/facebookresearch/faiss.git
cd faiss

2. Install Dependencies

  • CUDA Toolkit (installed via NVIDIA)
  • Visual Studio (install the “Desktop development with C++” workload)
  • Python (installed via Anaconda or Miniconda)
  • CMake and Ninja (install via Chocolatey or manually)

3. Configure CMake Build

Use the CMake GUI or command line:

cmake -B build -DFAISS_ENABLE_GPU=ON -DBLAS=Open -DCMAKE_CUDA_ARCHITECTURES=61 .

Replace 61 with your actual GPU architecture (e.g., 75 for Turing, 86 for Ampere).

4. Build Faiss

cmake --build build --config Release

This will build the required libraries under the build directory.

5. Add Faiss to Your Python Project

Manually build the Python bindings or create a .pyd file:

cd faiss/python
python setup.py install

Warning: This method is time-consuming and prone to compilation errors due to system-specific setups.

Option 3: Docker with GPU Support on Windows

Requirements:

  • Docker Desktop
  • WSL2 backend enabled
  • NVIDIA Container Toolkit

Steps:

  1. Enable WSL2 backend in Docker
  2. Install NVIDIA Container Toolkit for GPU passthrough
  3. Use a Faiss GPU Docker image:
docker run --gpus all -it pytorch/pytorch bash
pip install faiss-gpu

This method is ideal for isolating your Faiss environment and avoiding native installation issues.

Testing Faiss GPU Installation

Once Faiss is installed, run a simple Python test:

import faiss
import numpy as np

res = faiss.StandardGpuResources()
d = 64
index = faiss.IndexFlatL2(d)
gpu_index = faiss.index_cpu_to_gpu(res, 0, index)

data = np.random.random((10000, d)).astype('float32')
gpu_index.add(data)
query = np.random.random((5, d)).astype('float32')
D, I = gpu_index.search(query, k=5)
print("Top 5 nearest neighbors:", I)

Common Errors and Troubleshooting

Error: no module named faiss

  • Ensure you installed the GPU version (faiss-gpu) and not the CPU version.

Error: CUDA driver not found

  • Check if NVIDIA drivers and CUDA toolkit are installed correctly.

Error: unspecified launch failure

  • May be due to GPU memory overflow. Try reducing batch size or index size.

Faiss won’t build

  • Verify that all dependencies (Visual Studio, CUDA, CMake) are correctly installed and paths are set.

Performance Tips

  • Batch operations: Add or query vectors in batches to avoid memory overload.
  • Index type: Use IndexIVFPQ for large-scale datasets to reduce memory usage.
  • Memory monitoring: Use tools like nvidia-smi to track GPU usage.
  • Environment isolation: Use Conda or Docker for clean setups.

Final Thoughts

Installing Faiss for GPU on Windows can be challenging due to the lack of official support, but it’s definitely possible with the right setup. For most users, leveraging WSL2 or Docker is the most reliable way to get GPU acceleration without struggling with CMake and Visual Studio issues.

Once installed, Faiss on GPU can handle massive vector workloads with blazing speed, making it a critical tool for AI engineers and ML researchers building search engines, LLM pipelines, and recommender systems.

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